Load data from All.RData

rm(list=ls())  # clean up workspace
load("/Users/Xiang/GitFolders/IGCCodonSimulation/All.RData")

# paml.path <- "/Users/Xiang/GitFolders/IGCCodonSimulation/"
# IGC.geo.list <- c(3.0)
# 
# # Read in new PAML results
# data.path <- paste(paralog, "",sep = "/")
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
# }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
# }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
#   }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
# }
# 
# save.image("/Users/Xiang/GitFolders/IGCCodonSimulation/All.RData")

Feb 5 update

Check PAML estimate of two same tree topologies but different order of taxa

# Simulation using estimated Tau value of 1.409408
# Look at difference of each estimate (Tree 1 - Tree 2)

# log likelihood
summary(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0e+00   0e+00   0e+00   1e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
## [1] 9.999999e-08
# kappa
summary(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -2.0e-05  0.0e+00  0.0e+00  5.4e-06  0.0e+00  5.9e-04
sd(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
## [1] 5.929587e-05
# omega
summary(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -1e-05   0e+00   0e+00   0e+00   0e+00   1e-05
sd(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
## [1] 1.421338e-06
# N0_N6
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_N6", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0227300 -0.0053960  0.0000000  0.0001434  0.0048260  0.0313200
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_N6", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
## [1] 0.009121357
# N0_kluyveriYDR418W
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -2e-06   0e+00   0e+00  -7e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
## [1] 3.554766e-07
# N0_N1
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N1", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0313200 -0.0048260  0.0000000 -0.0001433  0.0053960  0.0227300
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N1", ])
## [1] 0.009121368
# N1_N2
summary(PAML_estimatedTau_3.0_1stTree_summary["N1_N2", ] - PAML_estimatedTau_3.0_2ndTree_summary["N1_N2", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.107100 -0.030280 -0.004677 -0.001543  0.027870  0.108800
sd(PAML_estimatedTau_3.0_1stTree_summary["N1_N2", ] - PAML_estimatedTau_3.0_2ndTree_summary["N1_N2", ])
## [1] 0.04205227
# N1_castelliiYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N1_castelliiYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N1_castelliiYEL054C", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.112300 -0.029140 -0.002168 -0.002559  0.028700  0.096270
sd(PAML_estimatedTau_3.0_1stTree_summary["N1_castelliiYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N1_castelliiYEL054C", ])
## [1] 0.04143213
# N2_bayanusYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N2_bayanusYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N2_bayanusYEL054C", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0726800 -0.0159300 -0.0008145 -0.0011340  0.0175900  0.0457300
sd(PAML_estimatedTau_3.0_1stTree_summary["N2_bayanusYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N2_bayanusYEL054C", ])
## [1] 0.02459005
# N2_N3
summary(PAML_estimatedTau_3.0_1stTree_summary["N2_N3", ] - PAML_estimatedTau_3.0_2ndTree_summary["N2_N3", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.078990 -0.018710 -0.002986 -0.002527  0.012510  0.054650
sd(PAML_estimatedTau_3.0_1stTree_summary["N2_N3", ] - PAML_estimatedTau_3.0_2ndTree_summary["N2_N3", ])
## [1] 0.02495729
# N3_N4
summary(PAML_estimatedTau_3.0_1stTree_summary["N3_N4", ] - PAML_estimatedTau_3.0_2ndTree_summary["N3_N4", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.192800 -0.086190 -0.038820 -0.043530  0.006872  0.064300
sd(PAML_estimatedTau_3.0_1stTree_summary["N3_N4", ] - PAML_estimatedTau_3.0_2ndTree_summary["N3_N4", ])
## [1] 0.05665155
# N3_kudriavzeviiYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N3_kudriavzeviiYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N3_kudriavzeviiYEL054C", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.073470 -0.004563  0.047630  0.044260  0.085770  0.168100
sd(PAML_estimatedTau_3.0_1stTree_summary["N3_kudriavzeviiYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N3_kudriavzeviiYEL054C", ])
## [1] 0.05684405
# N4_N5
summary(PAML_estimatedTau_3.0_1stTree_summary["N4_N5", ] - PAML_estimatedTau_3.0_2ndTree_summary["N4_N5", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.047830 -0.021610 -0.001236 -0.003519  0.008070  0.057790
sd(PAML_estimatedTau_3.0_1stTree_summary["N4_N5", ] - PAML_estimatedTau_3.0_2ndTree_summary["N4_N5", ])
## [1] 0.0226982
# N4_mikataeYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N4_mikataeYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N4_mikataeYEL054C", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.092700 -0.028990  0.002551 -0.002642  0.024200  0.067500
sd(PAML_estimatedTau_3.0_1stTree_summary["N4_mikataeYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N4_mikataeYEL054C", ])
## [1] 0.03476582
# N5_paradoxusYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N5_paradoxusYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N5_paradoxusYEL054C", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0496500 -0.0188700 -0.0005485 -0.0013410  0.0163500  0.0464800
sd(PAML_estimatedTau_3.0_1stTree_summary["N5_paradoxusYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N5_paradoxusYEL054C", ])
## [1] 0.022019
# N5_cerevisiaeYEL054C
summary(PAML_estimatedTau_3.0_1stTree_summary["N5_cerevisiaeYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N5_cerevisiaeYEL054C", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.096710 -0.028820 -0.008231 -0.004714  0.020370  0.089030
sd(PAML_estimatedTau_3.0_1stTree_summary["N5_cerevisiaeYEL054C", ] - PAML_estimatedTau_3.0_2ndTree_summary["N5_cerevisiaeYEL054C", ])
## [1] 0.03559505

Now start to check branches ratio of subtree branches of paralog 1 over paralog 2 ratios

# N0_N1
target <- PAML_3.0_summary["N0_N1", ] / PAML_3.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 682.5957
## [1] 1311.021
# N1_N2
target <- PAML_3.0_summary["N1_N2", ] / PAML_3.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.009262
## [1] 0.2219263
# N1_castellii
target <- PAML_3.0_summary["N1_castelliiYDR418W", ] / PAML_3.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.010218
## [1] 0.2422185
# N2_N3
target <- PAML_3.0_summary["N2_N3", ] / PAML_3.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 396.4708
## [1] 1982.431
# N2_bayanus
target <- PAML_3.0_summary["N2_bayanusYDR418W", ] / PAML_3.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.135669
## [1] 0.6685038
# N3_N4
target <- PAML_3.0_summary["N3_N4", ] / PAML_3.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 1.500644
## [1] 1.810983
# N3_kudriavzevii
target <- PAML_3.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_3.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.039909
## [1] 0.32901
# N4_N5
target <- PAML_3.0_summary["N4_N5", ] / PAML_3.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 0.9775722
## [1] 1.313046
# N4_mikatae
target <- PAML_3.0_summary["N4_mikataeYDR418W", ] / PAML_3.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 1.866805
## [1] 1.963559
# N5_paradoxus
target <- PAML_3.0_summary["N5_paradoxusYDR418W", ] / PAML_3.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 1.377084
## [1] 1.532407
# N5_cerevisiae
target <- PAML_3.0_summary["N5_cerevisiaeYDR418W", ] / PAML_3.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.041587
## [1] 0.5020038
# N0_N1
target <- PAML_10.0_summary["N0_N1", ] / PAML_10.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 808.1555
## [1] 1662.984
# N1_N2
target <- PAML_10.0_summary["N1_N2", ] / PAML_10.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.071101
## [1] 0.281692
# N1_castellii
target <- PAML_10.0_summary["N1_castelliiYDR418W", ] / PAML_10.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.025923
## [1] 0.3345364
# N2_N3
target <- PAML_10.0_summary["N2_N3", ] / PAML_10.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 161.1146
## [1] 1169.193
# N2_bayanus
target <- PAML_10.0_summary["N2_bayanusYDR418W", ] / PAML_10.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 32.60408
## [1] 313.9553
# N3_N4
target <- PAML_10.0_summary["N3_N4", ] / PAML_10.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 1.538383
## [1] 1.689708
# N3_kudriavzevii
target <- PAML_10.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_10.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.1444
## [1] 0.4797683
# N4_N5
target <- PAML_10.0_summary["N4_N5", ] / PAML_10.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 211.0151
## [1] 1481.767
# N4_mikatae
target <- PAML_10.0_summary["N4_mikataeYDR418W", ] / PAML_10.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 113.2484
## [1] 1108.817
# N5_paradoxus
target <- PAML_10.0_summary["N5_paradoxusYDR418W", ] / PAML_10.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 1.290973
## [1] 1.112757
# N5_cerevisiae
target <- PAML_10.0_summary["N5_cerevisiaeYDR418W", ] / PAML_10.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.040702
## [1] 0.568822
# N0_N1
target <- PAML_50.0_summary["N0_N1", ] / PAML_50.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 543.9516
## [1] 1426.697
# N1_N2
target <- PAML_50.0_summary["N1_N2", ] / PAML_50.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.073097
## [1] 0.2957784
# N1_castellii
target <- PAML_50.0_summary["N1_castelliiYDR418W", ] / PAML_50.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.020654
## [1] 0.2728166
# N2_N3
target <- PAML_50.0_summary["N2_N3", ] / PAML_50.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 1.352344
## [1] 1.576323
# N2_bayanus
target <- PAML_50.0_summary["N2_bayanusYDR418W", ] / PAML_50.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.459676
## [1] 1.934382
# N3_N4
target <- PAML_50.0_summary["N3_N4", ] / PAML_50.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 43.96184
## [1] 424.0525
# N3_kudriavzevii
target <- PAML_50.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_50.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.166184
## [1] 0.7123563
# N4_N5
target <- PAML_50.0_summary["N4_N5", ] / PAML_50.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1.407367
## [1] 1.702416
# N4_mikatae
target <- PAML_50.0_summary["N4_mikataeYDR418W", ] / PAML_50.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 186.1275
## [1] 1843.195
# N5_paradoxus
target <- PAML_50.0_summary["N5_paradoxusYDR418W", ] / PAML_50.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 101.6888
## [1] 572.1677
# N5_cerevisiae
target <- PAML_50.0_summary["N5_cerevisiaeYDR418W", ] / PAML_50.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.336023
## [1] 0.9247196
 # N0_N1
target <- PAML_100.0_summary["N0_N1", ] / PAML_100.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 702.8846
## [1] 1764.081
# N1_N2
target <- PAML_100.0_summary["N1_N2", ] / PAML_100.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.049549
## [1] 0.3781572
# N1_castellii
target <- PAML_100.0_summary["N1_castelliiYDR418W", ] / PAML_100.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.032278
## [1] 0.2838787
# N2_N3
target <- PAML_100.0_summary["N2_N3", ] / PAML_100.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 89.56181
## [1] 539.3573
# N2_bayanus
target <- PAML_100.0_summary["N2_bayanusYDR418W", ] / PAML_100.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.386252
## [1] 1.510943
# N3_N4
target <- PAML_100.0_summary["N3_N4", ] / PAML_100.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 888.6468
## [1] 5070.871
# N3_kudriavzevii
target <- PAML_100.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_100.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.237806
## [1] 0.9738664
# N4_N5
target <- PAML_100.0_summary["N4_N5", ] / PAML_100.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1.44583
## [1] 3.041035
# N4_mikatae
target <- PAML_100.0_summary["N4_mikataeYDR418W", ] / PAML_100.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 2.461894
## [1] 3.708844
# N5_paradoxus
target <- PAML_100.0_summary["N5_paradoxusYDR418W", ] / PAML_100.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 845.0949
## [1] 7179.084
# N5_cerevisiae
target <- PAML_100.0_summary["N5_cerevisiaeYDR418W", ] / PAML_100.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.472391
## [1] 1.872967
 # N0_N1
target <- PAML_500.0_summary["N0_N1", ] / PAML_500.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 390.7935
## [1] 1292.949
# N1_N2
target <- PAML_500.0_summary["N1_N2", ] / PAML_500.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1219.064
## [1] 8571.207
# N1_castellii
target <- PAML_500.0_summary["N1_castelliiYDR418W", ] / PAML_500.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.067131
## [1] 0.2984771
# N2_N3
target <- PAML_500.0_summary["N2_N3", ] / PAML_500.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 1309.071
## [1] 6196.195
# N2_bayanus
target <- PAML_500.0_summary["N2_bayanusYDR418W", ] / PAML_500.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 547.7266
## [1] 4709.178
# N3_N4
target <- PAML_500.0_summary["N3_N4", ] / PAML_500.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 605.8042
## [1] 2709.521
# N3_kudriavzevii
target <- PAML_500.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_500.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.347045
## [1] 1.256499
# N4_N5
target <- PAML_500.0_summary["N4_N5", ] / PAML_500.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1018.097
## [1] 9689.183
# N4_mikatae
target <- PAML_500.0_summary["N4_mikataeYDR418W", ] / PAML_500.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 722.6354
## [1] 3206.462
# N5_paradoxus
target <- PAML_500.0_summary["N5_paradoxusYDR418W", ] / PAML_500.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 140.7491
## [1] 824.1837
# N5_cerevisiae
target <- PAML_500.0_summary["N5_cerevisiaeYDR418W", ] / PAML_500.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.35265
## [1] 1.520308